Results are summarized in Tab. REF  for Structured3D and in Tab. REF 
for SceneCAD.
We compare our method to a range of prior approaches that can be grouped in two broad categories:
Floor-SP [1]} and MonteFloor [2]} both rely on Mask R-CNN to segment rooms, followed by learning-free optimization techniques to recover the floorplan.
HAWP [3]} and LETR [4]} are originally generic methods to detect line segments and have been adapted to floorplan reconstruction in [5]}.
HEAT [5]} is an end-to-end trainable neural model that first detects corners, then links them via edge classification.
Our RoomFormer outperforms all previous methods on Structured3D (Tab. REF ), increasing the F1 score by +\(1.9\)  for rooms, +\(4.7\)  for corners and +\(2.9\)  for angles from the previous state-of-the-art, HEAT.
Our RoomFormer is the fastest one among the tested methods, with more than 10 times faster inference than HEAT.
MonteFloor additionally employs a Douglas-Peucker algorithm [7]} for post-processing to simplify the topology of the output polygons. By contrast, RoomFormer does
not rely on any post-processing steps.
